初识聚类算法: DBSACN，初识聚类算法dbsacn,[Python]代码#

初识聚类算法: DBSACN，初识聚类算法dbsacn,[Python]代码#

[Python]代码

# scoding=utf-8import pylab as plfrom collections import defaultdict,Counterpoints = [[int(eachpoint.split("#")[0]), int(eachpoint.split("#")[1])] for eachpoint in open("points","r")]# 计算每个数据点相邻的数据点，邻域定义为以该点为中心以边长为2*EPs的网格Eps = 10surroundPoints = defaultdict(list)for idx1,point1 in enumerate(points):    for idx2,point2 in enumerate(points):        if (idx1 < idx2):            if(abs(point1[0]-point2[0])<=Eps and abs(point1[1]-point2[1])<=Eps):                surroundPoints[idx1].append(idx2)                surroundPoints[idx2].append(idx1)# 定义邻域内相邻的数据点的个数大于4的为核心点MinPts = 5corePointIdx = [pointIdx for pointIdx,surPointIdxs in surroundPoints.iteritems() if len(surPointIdxs)>=MinPts]# 邻域内包含某个核心点的非核心点，定义为边界点borderPointIdx = []for pointIdx,surPointIdxs in surroundPoints.iteritems():    if (pointIdx not in corePointIdx):        for onesurPointIdx in surPointIdxs:            if onesurPointIdx in corePointIdx:                borderPointIdx.append(pointIdx)                break# 噪音点既不是边界点也不是核心点noisePointIdx = [pointIdx for pointIdx in range(len(points)) if pointIdx not in corePointIdx and pointIdx not in borderPointIdx]corePoint = [points[pointIdx] for pointIdx in corePointIdx] borderPoint = [points[pointIdx] for pointIdx in borderPointIdx]noisePoint = [points[pointIdx] for pointIdx in noisePointIdx]# pl.plot([eachpoint[0] for eachpoint in corePoint], [eachpoint[1] for eachpoint in corePoint], 'or')# pl.plot([eachpoint[0] for eachpoint in borderPoint], [eachpoint[1] for eachpoint in borderPoint], 'oy')# pl.plot([eachpoint[0] for eachpoint in noisePoint], [eachpoint[1] for eachpoint in noisePoint], 'ok')groups = [idx for idx in range(len(points))]# 各个核心点与其邻域内的所有核心点放在同一个簇中for pointidx,surroundIdxs in surroundPoints.iteritems():    for oneSurroundIdx in surroundIdxs:        if (pointidx in corePointIdx and oneSurroundIdx in corePointIdx and pointidx < oneSurroundIdx):            for idx in range(len(groups)):                if groups[idx] == groups[oneSurroundIdx]:                    groups[idx] = groups[pointidx]# 边界点跟其邻域内的某个核心点放在同一个簇中for pointidx,surroundIdxs in surroundPoints.iteritems():    for oneSurroundIdx in surroundIdxs:        if (pointidx in borderPointIdx and oneSurroundIdx in corePointIdx):            groups[pointidx] = groups[oneSurroundIdx]            break# 取簇规模最大的5个簇wantGroupNum = 3finalGroup = Counter(groups).most_common(3)finalGroup = [onecount[0] for onecount in finalGroup]group1 = [points[idx] for idx in xrange(len(points)) if groups[idx]==finalGroup[0]]group2 = [points[idx] for idx in xrange(len(points)) if groups[idx]==finalGroup[1]]group3 = [points[idx] for idx in xrange(len(points)) if groups[idx]==finalGroup[2]]pl.plot([eachpoint[0] for eachpoint in group1], [eachpoint[1] for eachpoint in group1], 'or')pl.plot([eachpoint[0] for eachpoint in group2], [eachpoint[1] for eachpoint in group2], 'oy')pl.plot([eachpoint[0] for eachpoint in group3], [eachpoint[1] for eachpoint in group3], 'og')# 打印噪音点，黑色pl.plot([eachpoint[0] for eachpoint in noisePoint], [eachpoint[1] for eachpoint in noisePoint], 'ok')   pl.show()

DBscan.png

imgs/asCode/22213136_fZaP.png